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https://issues.apache.org/jira/browse/BEAM-10983?focusedWorklogId=523340&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-523340
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ASF GitHub Bot logged work on BEAM-10983:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 11/Dec/20 23:46
Start Date: 11/Dec/20 23:46
Worklog Time Spent: 10m
Work Description: rosetn commented on a change in pull request #12963:
URL: https://github.com/apache/beam/pull/12963#discussion_r541421022
##########
File path: website/www/site/content/en/get-started/from-spark.md
##########
@@ -0,0 +1,268 @@
+---
+title: "Getting started from Apache Spark"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Getting started from Apache Spark
+
+{{< localstorage language language-py >}}
+
+If you already know [_Apache Spark_](http://spark.apache.org/),
+learning _Apache Beam_ is familiar.
+The Beam and Spark APIs are similar, so you already know the basic concepts.
+
+Spark stores data _Spark DataFrames_ for structured data,
+and in _Resilient Distributed Datasets_ (RDD) for unstructured data.
+We are using RDDs for this guide.
+
+A Spark RDD represents a collection of elements,
+while in Beam it's called a _Parallel Collection_ (PCollection).
+A PCollection in Beam does _not_ have any ordering guarantees.
+
+Likewise, a transform in Beam is called a _Parallel Transform_ (PTransform).
+
+Here are some examples of common operations and their equivalent between
PySpark and Beam.
+
+## Overview
+
+Here's a simple example of a PySpark pipeline that takes the numbers from one
to four,
+multiplies them by two, adds all the values together, and prints the result.
+
+{{< highlight py >}}
+import pyspark
+
+sc = pyspark.SparkContext()
+result = (
+ sc.parallelize([1, 2, 3, 4])
+ .map(lambda x: x * 2)
+ .reduce(lambda x, y: x + y)
+)
+print(result)
+{{< /highlight >}}
+
+In Beam you _pipe_ your data through the pipeline using the
+_pipe operator_ `|` like `data | beam.Map(...)` instead of chaining
+methods like `data.map(...)`, but they're doing the same thing.
+
+Here's what an equivalent pipeline looks like in Beam.
+
+{{< highlight py >}}
+import apache_beam as beam
+
+with beam.Pipeline() as pipeline:
+ result = (
+ pipeline
+ | beam.Create([1, 2, 3, 4])
+ | beam.Map(lambda x: x * 2)
+ | beam.CombineGlobally(sum)
+ | beam.Map(print)
+ )
+{{< /highlight >}}
+
+> ℹ️ Note that we called `print` inside a `Map` transform.
+> That's because we can only access the elements of a PCollection
+> from within a PTransform.
+
+Another thing to note is that Beam pipelines are constructed _lazily_.
+This means that when you pipe `|` data you're only _decalring_ the
Review comment:
typo--declaring
##########
File path: website/www/site/content/en/get-started/from-spark.md
##########
@@ -0,0 +1,268 @@
+---
+title: "Getting started from Apache Spark"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Getting started from Apache Spark
+
+{{< localstorage language language-py >}}
+
+If you already know [_Apache Spark_](http://spark.apache.org/),
+learning _Apache Beam_ is familiar.
+The Beam and Spark APIs are similar, so you already know the basic concepts.
+
+Spark stores data _Spark DataFrames_ for structured data,
+and in _Resilient Distributed Datasets_ (RDD) for unstructured data.
+We are using RDDs for this guide.
+
+A Spark RDD represents a collection of elements,
+while in Beam it's called a _Parallel Collection_ (PCollection).
+A PCollection in Beam does _not_ have any ordering guarantees.
+
+Likewise, a transform in Beam is called a _Parallel Transform_ (PTransform).
+
+Here are some examples of common operations and their equivalent between
PySpark and Beam.
+
+## Overview
+
+Here's a simple example of a PySpark pipeline that takes the numbers from one
to four,
+multiplies them by two, adds all the values together, and prints the result.
+
+{{< highlight py >}}
+import pyspark
+
+sc = pyspark.SparkContext()
+result = (
+ sc.parallelize([1, 2, 3, 4])
+ .map(lambda x: x * 2)
+ .reduce(lambda x, y: x + y)
+)
+print(result)
+{{< /highlight >}}
+
+In Beam you _pipe_ your data through the pipeline using the
+_pipe operator_ `|` like `data | beam.Map(...)` instead of chaining
+methods like `data.map(...)`, but they're doing the same thing.
+
+Here's what an equivalent pipeline looks like in Beam.
+
+{{< highlight py >}}
+import apache_beam as beam
+
+with beam.Pipeline() as pipeline:
+ result = (
+ pipeline
+ | beam.Create([1, 2, 3, 4])
+ | beam.Map(lambda x: x * 2)
+ | beam.CombineGlobally(sum)
+ | beam.Map(print)
+ )
+{{< /highlight >}}
+
+> ℹ️ Note that we called `print` inside a `Map` transform.
+> That's because we can only access the elements of a PCollection
+> from within a PTransform.
+
+Another thing to note is that Beam pipelines are constructed _lazily_.
+This means that when you pipe `|` data you're only _decalring_ the
+transformations and the order you want them to happen,
+but the actual computation doesn't happen.
+The pipeline is run _after_ the `with beam.Pipeline() as pipeline` context has
+closed.
+
+> ℹ️ When the `with beam.Pipeline() as pipeline` context closes,
+> it implicitly calls `pipeline.run()` which triggers the computation to
happen.
+
+The pipeline is then sent to your
+[runner of
choice](https://beam.apache.org/documentation/runners/capability-matrix/)
+and it processes the data.
+
+> ℹ️ The pipeline can run locally with the _DirectRunner_,
+> or in a distributed runner such as Flink, Spark, or Dataflow.
+> The Spark runner is not related to PySpark.
+
+A label can optionally be added to a transform using the
+_right shift operator_ `>>` like `data | 'My description' >> beam.Map(...)`.
+This serves both as comments and makes your pipeline easier to debug.
+
+This is how the pipeline looks after adding labels.
+
+{{< highlight py >}}
+import apache_beam as beam
+
+with beam.Pipeline() as pipeline:
+ result = (
+ pipeline
+ | 'Create numbers' >> beam.Create([1, 2, 3, 4])
+ | 'Multiply by two' >> beam.Map(lambda x: x * 2)
+ | 'Sum everything' >> beam.CombineGlobally(sum)
+ | 'Print results' >> beam.Map(print)
+ )
+{{< /highlight >}}
+
+## Setup
+
+Here's a comparison on how to get started both in PySpark and Beam.
+
+{{< table >}}
+<table>
+<tr>
+ <th></th>
+ <th>PySpark</th>
+ <th>Beam</th>
+</tr>
+<tr>
+ <td><b>Install</b></td>
+ <td><code>$ pip install pyspark</code></td>
+ <td><code>$ pip install apache-beam</code></td>
+</tr>
+<tr>
+ <td><b>Imports</b></td>
+ <td><code>import pyspark</code></td>
+ <td><code>import apache_beam as beam</code></td>
+</tr>
+<tr>
+ <td><b>Creating a<br>local pipeline</b></td>
+ <td>
+ <code>sc = pyspark.SparkContext() as sc:</code><br>
+ <code># Your pipeline code here.</code>
+ </td>
+ <td>
+ <code>with beam.Pipeline() as pipeline:</code><br>
+ <code> # Your pipeline code here.</code>
+ </td>
+</tr>
+<tr>
+ <td><b>Creating values</b></td>
+ <td><code>values = sc.parallelize([1, 2, 3, 4])</code></td>
+ <td><code>values = pipeline | beam.Create([1, 2, 3, 4])</code></td>
+</tr>
+<tr>
+ <td><b>Creating<br>key-value pairs</b></td>
+ <td>
+ <code>pairs = sc.parallelize([</code><br>
+ <code> ('key1', 'value1'),</code><br>
+ <code> ('key2', 'value2'),</code><br>
+ <code> ('key3', 'value3'),</code><br>
+ <code>])</code>
+ </td>
+ <td>
+ <code>pairs = pipeline | beam.Create([</code><br>
+ <code> ('key1', 'value1'),</code><br>
+ <code> ('key2', 'value2'),</code><br>
+ <code> ('key3', 'value3'),</code><br>
+ <code>])</code>
+ </td>
+</tr>
+<tr>
+ <td><b>Running a<br>local pipeline</b></td>
+ <td><code>$ spark-submit spark_pipeline.py</code></td>
+ <td><code>$ python beam_pipeline.py</code></td>
+</tr>
+</table>
+{{< /table >}}
+
+## Transforms
+
+Here are the equivalents of some common transforms in both PySpark and Beam.
+
+{{< table >}}
+|
| PySpark | Beam
|
+|----------------------------------------------------------------------------------|---------------------------------------|---------------------------------------------------------|
+| [**Map**](/documentation/transforms/python/elementwise/map/)
| `values.map(lambda x: x * 2)` | `values | beam.Map(lambda x: x *
2)` |
+| [**Filter**](/documentation/transforms/python/elementwise/filter/)
| `values.filter(lambda x: x % 2 == 0)` | `values | beam.Filter(lambda x: x
% 2 == 0)` |
+| [**FlatMap**](/documentation/transforms/python/elementwise/flatmap/)
| `values.flatMap(lambda x: range(x))` | `values | beam.FlatMap(lambda x:
range(x))` |
+| [**Group by key**](/documentation/transforms/python/aggregation/groupbykey/)
| `pairs.groupByKey()` | `pairs | beam.GroupByKey()`
|
+| [**Reduce**](/documentation/transforms/python/aggregation/combineglobally/)
| `values.reduce(lambda x, y: x+y)` | `values |
beam.CombineGlobally(sum)` |
+| [**Reduce by
key**](/documentation/transforms/python/aggregation/combineperkey/) |
`pairs.reduceByKey(lambda x, y: x+y)` | `pairs | beam.CombinePerKey(sum)`
|
+| [**Distinct**](/documentation/transforms/python/aggregation/distinct/)
| `values.distinct()` | `values | beam.Distinct()`
|
+| [**Count**](/documentation/transforms/python/aggregation/count/)
| `values.count()` | `values |
beam.combiners.Count.Globally()` |
+| [**Count by key**](/documentation/transforms/python/aggregation/count/)
| `pairs.countByKey()` | `pairs |
beam.combiners.Count.PerKey()` |
+| [**Take smallest**](/documentation/transforms/python/aggregation/top/)
| `values.takeOrdered(3)` | `values |
beam.combiners.Top.Smallest(3)` |
+| [**Take largest**](/documentation/transforms/python/aggregation/top/)
| `values.takeOrdered(3, lambda x: -x)` | `values |
beam.combiners.Top.Largest(3)` |
+| [**Random sample**](/documentation/transforms/python/aggregation/sample/)
| `values.takeSample(False, 3)` | `values |
beam.combiners.Sample.FixedSizeGlobally(3)` |
+| [**Union**](/documentation/transforms/python/other/flatten/)
| `values.union(otherValues)` | `(values, otherValues) |
beam.Flatten()` |
+| [**Co-group**](/documentation/transforms/python/aggregation/cogroupbykey/)
| `pairs.cogroup(otherPairs)` | `{'Xs': pairs, 'Ys': otherPairs}
| beam.CoGroupByKey()` |
+{{< /table >}}
+
+> ℹ️ To learn more about the transforms available in Beam, check the
+> [Python transform gallery](/documentation/transforms/python/overview).
+
+## Using calculated values
+
+Since we are working in potentially distributed environments,
+we can't guarantee that the results we've calculated are available at any
given machine.
+
+In PySpark, we can get a result from a collection of elements (RDD) by using
+`data.collect()`, or other aggregations such as `reduce()`, `count()` and more.
Review comment:
comm after `count()`
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Issue Time Tracking
-------------------
Worklog Id: (was: 523340)
Time Spent: 4h 20m (was: 4h 10m)
> Have a getting started for Spark users
> --------------------------------------
>
> Key: BEAM-10983
> URL: https://issues.apache.org/jira/browse/BEAM-10983
> Project: Beam
> Issue Type: New Feature
> Components: website
> Reporter: David Cavazos
> Assignee: David Cavazos
> Priority: P2
> Time Spent: 4h 20m
> Remaining Estimate: 0h
>
> Have a friendlier getting started experience for users who already know Spark.
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